Sleep states and other brain activity have been commonly analyzed via electroencephalography or EEG signals. As a person falls asleep, the brain activity is modulated, representing different depths and phases of sleep. In a typical person, the sleep states transition over time, starting at a first sleep state known as slow wave sleep or SWS. SWS has low frequency high power EEG activity. The sleep may lighten into so-called intermediate sleep states. Other sleep states known as rapid eye movement sleep is characterized by a lower power EEG activity.
EEG signals follow a distribution where higher frequency signals have lower amplitudes and therefore lower power. This so-called 1/f distribution means that the highest amplitudes are present at the lowest frequencies.
EEG signals for sleep stage determination are conventionally analyzed using the Rechtschaffen-Kales method. This method can rely on manually scoring sleep EEG signals due to the low power frequency limitations of automated signal analysis techniques. The Rechtschaffen-Kales method can be both highly unreliable and time consuming because statistically significant shifts at high frequencies are usually not detectable by a human scorer due to the very low amplitudes. Further, the Rechtschaffen-Kales method tends to have poor temporal and spatial resolution, does not make all of its variables known, and commonly leads to low inter-user agreement rates across both manual as well as automated scorers. Unfortunately, alternative sleep state determination methods, including artificial neural network classifiers, usually rely on multiple channels and tend to emulate human performance, thereby improving the time of determination without drastically improving quality.